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<h4> PCA Options</h4>

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<p><span class="style2">PCA options are available when you select the PCA type.
</span></p>

<ul>

<li> Standard </li>

<ul>

<li> Do You Want To Stack Datasets? - Options are 'Yes' and 'No'. </li>

<ul> 


<li> Yes - Data sets are stacked to compute covariance matrix. This option 
assumes that there is enough RAM available to stack the data sets and for 
computing covariance matrix. Please note that full storage of covariance matrix 
is required when you select this option. </li>

<li> No - A pair of data sets are loaded at a time to compute covariance matrix. 
This option uses less memory usage but it requires N*(N-1)/2 loops to compute 
the covariance matrix where N is the number of data sets. </li>

</ul>


<li> 'Select Matrix Storage Type' - Options are 'Full' and 'Packed'. You have 
the option to store only lower triangular portion of the symmetric matrix with 
the packed storage scheme. </li>

<li> 'Select Precision' - Options are 'Double' and 'Single'. Single precision 
uses 50% less memory required when compared to double precision. Single 
precision is accurate up to 7 digits after decimal point. </li>

<li> 'Select Eigen Solver Type' - Options are 'Selective' and 'All'. These 
options will be used only for the packed storage scheme.

<ul>

<li> 'Selective' - Only a few desired eigen values are computed. This option 
will compute eigen values faster when compared to 'All' option. However, if 
there are convergence issues use option 'All' to compute eigen values.</li>

<li> 'All' - All eigen values are computed. We recommend to use this option for 
computing eigen values only when the selective eigen solver doesn't converge.</li>

</ul>
</li>
</ul>

<li> Expectation Maximization (EM PCA) has fewer memory constraints and is 
advantageous over standard PCA when only few eigen values need to be computed 
from a large data-set. PCA options of this approach are discussed below: </li>

<ul>

<li> 'Do You Want To Stack Datasets?' - Options are 'Yes' and 'No'. </li>

<ul>

<li> 'Yes' - This option assumes that there is enough RAM available to stack the 
data sets. </li>

<li> 'No' - A data-set is loaded at a time to compute transformation matrix at 
each iteration. This option may take days to solve the problem if there are very 
large data-sets. </li>

</ul>


<li> 'Select Precision' - Options are 'Double' and 'Single'.</li>

<li> 'Select Stopping Tolerance' - Norm of residual error is used. Residual 
error is computed by subtracting the transformation matrix at the current 
iteration from the previous iteration. </li>

<li> 'Enter Max No. Of Iterations' - Enter maximum number of iterations to use.</li>

</ul>

<strong>Note:

</strong>

<ul>

<li> Before setting up analysis, please see &quot;icatb_mem_ica.m&quot; script to get 
a close estimate of the RAM required for all the analysis types. In general for 
better performance, stack data-sets using single precision. However, if memory 
is an issue don't stack data-sets and use slower ways to compute PCA (EM PCA or 
packed storage scheme of standard PCA).</li>

<li> By default, GIFT will save MAT files in the uncompressed format 
('-v6'). Always use uncompressed format if you want a better performance 
during the analysis phase.</li>

</ul>

</ul>


<p class="style1"> Figure 1: PCA Options </p>

<p class="style1">
<img src="gift/images/icatb_pca_options.jpg">

</p>

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